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Molecular Endocrinology, doi:10.1210/me.2007-0484
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Molecular Endocrinology 22 (5): 1260-1273
Copyright © 2008 by The Endocrine Society

Systematic Determination of Differential Gene Expression in the Primate Corpus Luteum during the Luteal Phase of the Menstrual Cycle

Randy L. Bogan, Melinda J. Murphy, Richard L. Stouffer and Jon D. Hennebold

Division of Reproductive Sciences (R.L.B., M.J.M., R.L.S., J.D.H.), Oregon National Primate Research Center, Oregon Health & Science University West Campus, Beaverton, Oregon 97006; and Department of Obstetrics & Gynecology (R.L.S., J.D.H.), Oregon Health & Science University, Portland, Oregon 97239

Address all correspondence and requests for reprints to: Jon D. Hennebold, Oregon Health and Science University, Oregon National Primate Research Center, 505 Northwest 185th Avenue, Beaverton, Oregon 97006. E-mail: henneboj{at}ohsu.edu.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
The molecular and cellular processes required for development, function, and regression of the primate corpus luteum (CL) are poorly defined. We hypothesized that there are dynamic changes in gene expression occurring during the CL life span, which represent proteins and pathways critical to its regulation. Therefore, a genomic approach was utilized to systematically identify differentially expressed genes in the rhesus macaque CL during the luteal phase of natural menstrual cycles. CL were collected between d 3–5 (early stage), d 7–8 (mid), d 10–12 (mid-late), d 14–16 (late), or d 18–19 (very-late) after the midcycle LH surge. From the early through very-late stages, 3234 transcripts were differentially expressed, with 879 occurring from the early through late stages that encompass the processes of luteinization, maintenance, and functional regression. To characterize gene changes most relevant to these processes, ontology analysis was performed using the list of 879 differentially expressed transcripts. Four main groups of related genes were identified with relevance to luteal physiology including: 1) immune function; 2) hormone and growth factor signaling; 3) steroidogenesis; and 4) prostaglandin biosynthesis, metabolism, and signaling. A subset of genes representing each of the four major categories was selected for validation of microarray results by quantitative real-time PCR. Results in mRNA levels were similar between the two methodologies for 17 of 18 genes. Additionally, protein levels for three genes were determined by Western blot analysis to parallel mRNA levels. This database will facilitate the identification of many novel or previously underappreciated pathways that regulate the structure and function of the primate CL.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
THE CORPUS LUTEUM (CL) is a transient endocrine gland that forms from cells remaining in the follicle after ovulation and is the primary source of progesterone during the menstrual cycle and early pregnancy in primates. The life span of the CL during the menstrual cycle has three general phases: 1) luteinization, 2) maintenance, and 3) luteolysis. Luteinization is the process whereby the cells remaining in the follicle after ovulation hypertrophy, reorganize, and form the highly steroidogenic CL (1, 2, 3). The maintenance phase corresponds to the period of maximal progesterone secretion. If conception does not occur, luteolysis takes place whereby CL progesterone production ceases (functional regression), which is followed by major structural remodeling and apoptosis (structural regression) (4, 5). Thus, the relatively short life span of the CL is characterized by dramatic changes in both the structure and function of this gland. The individual genes and gene families that are dynamically regulated in the CL throughout its life span have yet to be defined. Therefore, a microarray-based approach was employed to systematically analyze the entire primate CL transcriptome during the luteal phase of the natural menstrual cycle.

We used the Affymetrix rhesus macaque genome DNA microarrays (Affymetrix, Santa Clara, CA; >47,000 transcripts represented), thereby allowing for the complete analysis of the macaque transcriptome. Previous studies have used CL collected from rhesus macaques between d 3–5 (early stage), d 7–8 (mid stage), d 10–12 (mid-late stage), d 14–16 (late stage), or d 18–19 (very-late stage) after the midcycle LH surge to investigate the structure and function of the CL during its life span (6, 7, 8, 9, 10, 11). The early CL are undergoing luteinization, the mid are fully functional CL that are at their peak progesterone-producing capacity, the midlate stage is a transitional period in which CL are still producing significant quantities of progesterone but are nearing the time when luteolysis initiates in nonconception cycles, the late stage corresponds to CL undergoing functional regression (cessation of progesterone secretion), and the very-late stage (menses) is when the structural remodeling and apoptosis associated with luteolysis are occurring. Measuring mRNA levels in CL collected during these periods provides a comprehensive analysis of the primate transcriptome throughout its life span. Microarray data were normalized and filtered to define differentially expressed transcripts, and the filtered genes were analyzed to identify common groups, pathways, and biological themes. Also, microarray data for a select subset of genes were validated using quantitative real-time PCR (Q-PCR), and the corresponding protein levels were determined by Western blot analysis in a few cases.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Identification of Differentially Expressed Genes in the Macaque CL throughout the Luteal Phase
From the early through very-late stages of the luteal phase, 3234 transcripts met our criteria for differential expression (>2-fold change relative to early stage; ANOVA, P < 0.01) in the macaque CL. When excluding the very-late stage, the number of differentially expressed transcripts from the early through late stages was 879 (ANOVA; P < 0.01). Thus, the greatest change in gene expression occurred during the structural remodeling that takes place in the transition from the late (functionally regressing CL) to very-late (structurally regressing CL) stages (Fig. 1AGo). The majority of transcripts that were differentially expressed from the early through very-late stages exhibited a decrease in expression (3234 total: 1418 up-regulated and 1816 down-regulated). In contrast, the transcripts that were differentially expressed from the early through late stages were mostly up-regulated (879 total: 518 up-regulated and 361 down-regulated), indicating that the late to very-late CL transition predominantly involves suppression of gene expression. Because of the large number of transcripts that passed our criteria for differential expression only when the very-late stage was included, we elected to exclude the very-late stage from ontology analysis of the gene list that was intended to identify related groups of genes that are differentially expressed during the life span of the CL. This allowed us to focus on gene changes most relevant to luteinization, maintenance, and functional regression of the CL.


Figure 1
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  		Fig. 1. Patterns of Gene Expression in the Rhesus Macaque CL during the Luteal Phase of the Natural Menstrual Cycle

Panel A is a dendrogram that illustrates the relatedness between CL stages in terms of gene expression. The more branching that occurs, the more unrelated groups are. The scale bar is an arbitrary measurement of Euclidean distance. Panel B is a heat map for all the probe sets from the early through late stages that were differentially expressed using P < 0.01 (>2-fold change; ANOVA). Each column represents a stage of the luteal phase, and each row is a probe set corresponding to an individual transcript. Arrows indicate the approximate point where the pattern of gene expression changes. Less common patterns are observed in regions 1 and 3. Region 2 contains probe sets that demonstrated a relatively steady increase from the early through late stages, and region 4 contains the probe sets that had a relatively steady decrease in expression from the early through late stages of the luteal phase. Min, Minimum; Max, maximum.

 
There were two primary patterns of differential gene expression occurring from the early through late stages: 1) genes that displayed a relatively constant increase; and 2) genes that exhibited a relatively steady decrease (Fig. 1BGo). Less frequent expression patterns included: 1) genes that decreased from the early through mid stage and then increased from the mid through late stage; and 2) genes that were lowly expressed in the early CL, increased in the mid CL, displayed no change or slight increases/decreases from the mid to mid-late stage, and decreased in the late stage.

Molecular function ontology reports were generated and used to organize the differentially expressed transcripts (changes in mRNA levels significant at P < 0.01) into groups (Table 1Go). Significantly overrepresented ontologies (≥5 and ≤50 genes; z-score ≥ 2.0) (12) were further combined based on related function. Based on this objective and subjective analysis, four primary categories of related genes were identified in the list of differentially expressed genes and includes those involved in: 1) immune function; 2) hormone and growth factor signaling; 3) steroidogenesis; and 4) prostaglandin biosynthesis, metabolism, and signaling.


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  		Table 1. Significantly Overrepresented Gene Ontologies from the List of Differentially Expressed Genes in CL from the Early through Late Stages of the Macaque Luteal Phase

 
Validation of Microarray Expression Data
A total of 18 genes representing each of the four major categories identified were selected for quantification of mRNA levels by Q-PCR to validate the microarray results (Table 2Go).


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  		Table 2. Q-PCR Validation of Microarray Expression Data

 
Immune Function Genes
Several genes encoding major histocompatibility complex (MHC) class I and class II molecules were differentially expressed (>2-fold change; ANOVA, P < 0.05) in CL throughout the luteal phase. MHC class I and class II genes were coordinately regulated with their highest levels occurring during the late to very-late stages (Fig. 2AGo). Several cytokine receptors were also differentially expressed through the luteal phase. Of these, mRNA levels of the IL receptors 11{alpha} (IL11RA), 17D (IL17RD), and 13{alpha}2 (IL13RA2), as well as the leukemia inhibitory factor receptor {alpha} (LIFR), were measured by Q-PCR (Fig. 2BGo and Table 2Go). The resultant Q-PCR data validated the microarray mRNA expression results for all four genes. Levels of IL17RD mRNA steadily increased more than 3-fold from the early through late stages (P < 0.05), whereas IL13RA2 was significantly (P < 0.05) higher in early CL compared with all other stages (> 10-fold decrease early through very-late stages) as determined by both methodologies (Fig. 2BGo). The only minor discrepancy was for IL11RA, where both methods showed a consistent increase in its mRNA levels from the early through late stage (>2-fold total). The microarray data exhibited a significant decrease in IL11RA mRNA levels from the late to very-late stages, whereas Q-PCR showed no change between these two stages (Fig. 2BGo). The expression of immune system-related genes was usually highest around the time of luteolysis, although several were also highly expressed in the early CL when luteinization is occurring (Fig. 2AGo).


Figure 2
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  		Fig. 2. Differentially Expressed Immune Function Genes in Rhesus Macaque CL through the Luteal Phase

Panel A contains a heat map of representative immune function genes identified as differentially expressed (>2-fold change; ANOVA, P < 0.01). Each column is a stage of the luteal phase, and each row represents a single gene. All genes represented on this heat map were identified as differentially expressed using data from the early through late stages only (indicated by the bracket above the columns), although their corresponding expression levels in the very-late stage are shown as well. In panel B, the validation of microarray results by Q-PCR is displayed for three immune function genes. Microarray results are in black columns on the left axis, and Q-PCR results are in white columns on the right axis. Uppercase letters denote significant differences for microarray data (P < 0.05), and lowercase letters denote significant differences for Q-PCR data. Min, Minimum; Max, maximum.

 
Hormone and Growth Factor Signaling Genes
A number of genes encoding proteins that serve as growth factors or hormones and their receptors, as well as regulators of growth factor and hormone secretion or action, were differentially expressed (>2-fold change; ANOVA, P < 0.05) in the CL during the luteal phase (Fig. 3AGo). Genes involved in hormone and growth factor signaling that were validated by Q-PCR included: relaxin (RLN1), endothelin receptor B (EDNRB), prolactin receptor (PRLR), carboxypeptidase E (CPE), and IGF 1 receptor (IGF1R) (Fig. 3Go and Table 2Go). Of these, only the Q-PCR results for IGF1R did not match the microarray data, which exhibited high levels of IGF1R in early CL before significantly decreasing by the late stage, whereas the early stage was not different from other stages as determined by Q-PCR (Table 2Go).


Figure 3
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  		Fig. 3. Differentially Expressed Hormone and Growth Factor Signaling Genes in Rhesus Macaque CL through the Luteal Phase

Representative differentially expressed genes involved in hormone and growth factor signaling are displayed in the heat map in panel A. All genes represented on this heat map were identified as differentially expressed (>2-fold change; ANOVA, P < 0.01) using data from the early through late stages only (indicated by the bracket above the columns), although their corresponding expression levels in the very-late stage are shown as well. Panel B contains microarray and Q-PCR results for RLN1, CPE, and PRLR. Uppercase letters denote significant differences for microarray data (P < 0.05), and lowercase letters denote significant differences for Q-PCR data. Min, Minimum; Max, maximum.

 
RLN1 had the second greatest change in expression of all genes represented on the microarray. It increased steadily from the early through late stages (>30-fold), and these results were confirmed by Q-PCR (Fig. 3BGo). mRNA for CPE, the gene product of which is a membrane-associated protein that modifies peptide hormones, steadily increased from the early through late stages (>8-fold) as determined by both methodologies (Fig. 3BGo). Levels of PRLR mRNA significantly increased from the early to mid stage, remained high through the late stage (>3-fold total change), and declined to baseline in the very-late stage (Fig. 3BGo). EDNRB significantly (P < 0.05) increased from the early to mid stage by both microarray and Q-PCR and remained high through all subsequent stages (>3-fold total) (Table 2Go).

Steroidogenic Genes
Several genes known to be involved in LH-induced progesterone and estradiol production were identified as differentially expressed (>2-fold change; ANOVA, P < 0.05) in CL throughout the luteal phase (Fig. 4AGo). Of these, the LH receptor (LHCGR), 3β hydroxysteroid dehydrogenase {Delta}5/{Delta}4 isomerase 2 (HSD3B2), and cytochrome P450, family 19, subfamily A, polypeptide 1 (CYP19A1 or aromatase) genes were validated with regard to their mRNA expression through the macaque luteal phase (Fig. 4AGo). Levels of LHCGR mRNA significantly (P < 0.05) increased more than 2.5-fold from the early to mid-late stage and remained high in the late CL, then significantly (P < 0.05) decreased in the very-late CL. These findings were confirmed by Q-PCR (Fig. 4BGo). Expression of HSD3B2 mRNA was highest in the early through mid-late stages, and then significantly (P < 0.05) decreased in the late stage (>5-fold change early vs. late) followed by another significant decrease in the very-late CL as determined by both methodologies (Fig. 4BGo). The highest levels of CYP19A1 mRNA expression occurred in the mid through late stages (Fig. 4BGo) with the mid-late CL having significantly (P < 0.05) higher mRNA levels than either early (> 2.4-fold increase mid-late vs. early CL) or very-late CL as determined by microarray and Q-PCR.


Figure 4
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  		Fig. 4. Steroidogenic Gene Expression during the Rhesus Macaque Luteal Phase

Genes involved in steroidogenesis are displayed in the heat map in panel A. A single asterisk denotes differential expression using data from the early through very-late stages (>2-fold change; ANOVA, P < 0.01), whereas genes with a double asterisk were differentially expressed (>2-fold change; ANOVA, P < 0.05) when mRNA levels from the early through late stages only were compared (indicated by the bracket above the columns). Nondifferentially expressed genes associated with steroidogenesis are included for completeness. Panel B contains Q-PCR validation of microarray results for LHCGR, HSD3B2, and CYP19A1. Uppercase letters denote significant differences for microarray data (P < 0.05), and lowercase letters denote significant differences for Q-PCR data. Min, Minimum; Max, maximum.

 
Prostaglandin Biosynthesis, Metabolism, and Signaling
Genes involved in prostaglandin biosynthesis, metabolism, and transport; as well as prostacyclin (prostaglandin I), prostaglandin E2, and prostaglandin F2{alpha} signaling were analyzed (Fig. 5AGo). Genes encoding the prostaglandin transporter (SLCO2A1), prostaglandin-endoperoxide synthase 2 (PTGS2), prostaglandin E2 synthase (PTGES), prostaglandin E2 receptor subtype EP3 (PTGER3), and the prostaglandin F2{alpha} receptor (PTGFR) were differentially expressed (>2-fold change; ANOVA, P < 0.05) from the early through late stages as determined by microarray analysis (Fig. 5AGo). Changes in expression of these genes were substantial with PTGS2 displaying a more than 5-fold net decrease from early through late stages, and PTGES decreased more than 8-fold from the mid through late CL. PTGER3 had a more than 5-fold decrease from the early through late stages. Notably, PTGFR had the greatest change in expression of all the genes identified from the present microarray database, with a more than 31-fold increase from the early through late stages. Also, 15-hydroxyprostaglandin dehydrogenase (HPGD) and SLCO2A1 had greater than 2-fold net increases from the early through late stages, although only SLCO2A1 was statistically (ANOVA, P < 0.05) significant (Fig. 5AGo). Expression of PTGS2, HPGD, PTGES, PTGER3, and PTGFR were confirmed by Q-PCR, and results were very similar to microarray data in all cases (Fig. 5BGo and Table 2Go).


Figure 5
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  		Fig. 5. Expression of Genes Associated with Prostaglandin Biosynthesis, Metabolism, and Signaling during the Rhesus Macaque Luteal Phase

Genes involved in prostaglandin biosynthesis, metabolism, and signaling are displayed in the heat map in panel A. Genes with a single asterisk after their name met the criteria for differential expression (>2-fold change; ANOVA, P < 0.01) using data from the early through very-late stages, whereas genes with a double asterisk were differentially expressed (>2-fold change; ANOVA, P < 0.05) when analyzed from the early through late stages only (indicated by the bracket above the columns). Nondifferentially expressed genes associated with prostaglandin biosynthesis, metabolism, and signaling are included for completeness. Panel B contains Q-PCR and microarray results plotted next to each other for PTGS2, PTGES, PTGER3, and PTGFR. Uppercase letters denote significant differences for microarray data (P < 0.05), and lowercase letters denote significant differences for Q-PCR data. Min, Minimum; Max, maximum.

 
Corresponding Changes in Protein Expression for Select Differentially Expressed Genes
Protein levels of a select subset of differentially expressed genes were determined by Western blot analysis. A monoclonal antibody against human prolactin receptor (PRLR) was used to detect the macaque protein. Three bands were detected at approximately 90, 70, and 45 kDa (Fig. 6AGo). All bands are consistent with various reported isoforms of PRLR. Long and short forms have been identified at approximately 70 and 45 kDa in the rodent CL (13), respectively, and the mature, glycosylated form of PRLR migrates at 85–95 kDa in human decidua and placenta (14). The mature, glycosylated receptor appeared to be the most abundant form in our samples because it produced the strongest band intensity. All three forms displayed similar patterns of expression compared with microarray/Q-PCR results (see Fig. 3Go) with an increase from the early to mid-late stages, and highest levels in mid-late or late CL. The 90-kDa form was significantly (P < 0.05) higher in mid-late and late CL than all other stages(>4-fold increase early to mid-late stage), and levels of the 70-kDa form were significantly higher in late CL compared with all other stages (Fig. 6BGo). There were no statistically significant differences between stages for the 45-kDa form, although the pattern of expression was similar to the 90-kDa form.


Figure 6
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  		Fig. 6. Differential Expression of PRLR Protein

Panel A is a representative Western blot using samples pooled from CL collected at each stage. The approximate molecular weight for each form of PRLR is indicated. The lower image is β-tubulin (TUBB) expression on the same membrane to demonstrate equivalent protein loading. Panel B contains densitometry results for each of the three isoforms of PRLR detected. Levels of PRLR from individual CL (n = 4/stage) were normalized to β-tubulin, and the resultant ratio was analyzed by ANOVA followed by comparison between groups using the SNK test. Columns with different letters are significantly different (P < 0.05).

 
The polyclonal antibody used to detect macaque HSD3B2 detected a doublet at approximately 41–42 kDa, with the lower band of the doublet representing HSD3B2 as determined by preabsorption of the primary antibody with immunizing peptide (Fig. 7AGo). The pattern for HSD3B2 protein levels (Fig. 7BGo) was similar to mRNA levels as determined by microarray/Q-PCR (Fig. 4Go) with expression highest at the early through mid-late stages, followed by a significant (P < 0.05) 2-fold decrease in expression from the mid-late to late stage. The HSD3B2 protein was not detected in very-late CL.


Figure 7
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  		Fig. 7. Differential Expression of HSD3B2 Protein

Panel A is a Western blot using samples pooled from CL collected at each stage. The upper image is probed for HSD3B2 with the last three lanes containing pooled mid-late, late, and very-late stage samples that were probed with primary antibody that had been preabsorbed with immunizing peptide. The lower band of the doublet disappeared after preabsorption of the primary antibody, but not the upper band, indicating that only the lower band corresponds to HSD3B2. The lower image is β-tubulin (TUBB), which was used as a loading control. Levels of HSD3B2 from individual CL (n = 4/stage) were normalized to β-tubulin, and the resultant ratio was analyzed by ANOVA followed by comparison between groups using the SNK test. Columns with different letters are significantly different (P < 0.05).

 
CYP19A1 protein expression (Fig. 8Go) also paralleled the observed changes in mRNA levels (Fig. 4Go). There was a significant (P < 0.05) greater than 5-fold increase in CYP19A1 from the early to mid stage, and levels remained high through the mid-late stage, followed by a significant (P < 0.05) decrease back to baseline (early CL) levels in the very-late stage.


Figure 8
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  		Fig. 8. Differential Expression of CYP19A1 Protein

Panel A is a Western blot using samples pooled from CL collected at each stage. The upper image is CYP19A1, and the lower is β-tubulin (TUBB), which served as a loading control. Levels of CYP19A1 from individual CL (n = 4/stage) were normalized to β-tubulin, and the resultant ratio was analyzed by ANOVA followed by comparison between groups using the SNK test. Columns with different letters are significantly different (P < 0.05).

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 REFERENCES
 
As anticipated, there were changes in mRNA levels for many genes as the CL progressed through the various stages of the natural luteal life span. When we focused on the stages of the luteal phase associated with CL development, function, and functional regression, four main groups emerged from analysis of the list of differentially expressed genes: 1) immune function; 2) hormone and growth factor signaling; 3) steroidogenesis; and 4) prostaglandin biosynthesis, metabolism, and signaling. Although this is not an all-inclusive list and many other related groups of genes could be identified within our data, these four appear to be the most prominent and relevant to luteal physiology.

Genes involved in immune function usually had their highest levels of expression around the time of luteolysis (late or very-late stages). This supports the concept that the immune system plays a role in initiating and/or advancing luteolysis in primates (15). Because total CL homogenates were used to isolate RNA for microarray and Q-PCR analysis, changes in expression of some immune genes could be a result of an increase in the number of immune cells present in the CL in the late luteal phase (15, 16). To a lesser extent, there was also high expression of some immune function genes in the early stage when luteinization is occurring (e.g. IL13RA2; CD55 molecule; complement components 3, 4A, and 4B; and chemokine ligands 14 and 18), indicating a potential involvement of the immune system in luteal development. Furthermore, of all the differentially expressed genes related to immune function analyzed, none had their highest levels of expression in the mid or mid-late stages, indicating that there may be a suppression of immune system activity in the fully formed CL during peak luteal function. This is consistent with a potential immunosuppressive role of progesterone, which is produced at high quantities during these periods (15).

RLN1 is a peptide hormone produced by the CL (17). In nonprimates, RLN1 is a pregnancy-associated hormone that prepares the cervix and vagina for parturition (17), and circulating levels of RLN1 surge just before the onset of parturition. In monkeys and women, circulating RLN1 levels are highest during early pregnancy, the period when the CL is required for the maintenance of pregnancy (18, 19). Abnormal levels of RLN1 in circulation have been correlated with early pregnancy loss in women (20). This indicates that in primates, RLN1 may be related to CL function, pregnancy establishment, and/or maintenance instead of parturition. However, a physiological role for RLN1 in primates has not been demonstrated. Our finding that RLN1 mRNA levels are dramatically increased from the early through late stages support the possibility of a physiological role of this hormone in early pregnancy.

Carboxypeptidase E is a membrane-associated exopeptidase responsible for processing prohormones (21). It may serve as a prohormone-sorting receptor for the regulated secretory pathway (22, 23), and disruption of carboxypeptidase E-mediated sorting may cause elevated plasma levels of proinsulin, a hormone structurally related to RLN1 (24). In our data, CPE mRNA levels showed a nearly identical expression pattern as RLN1, indicating a potential role for this gene in secretion of RLN1 and other peptide hormones (e.g. oxytocin, inhibin/activin) from the CL, especially in the late luteal phase.

Prolactin has been identified as the primary luteotropin in rodents because pituitary secretion of prolactin during the first week of pregnancy or pseudopregnancy (25), release of prolactin-like luteotropins by the decidua and placenta from d 7–11 (26), as well as expression of placental lactogens from trophoblast cells in later pregnancy (27) result in maintenance of luteal structure-function. Prolactin, prolactin-like luteotropins, and placental lactogen all bind to PRLR (2). However, there is little direct evidence of a role for prolactin in regulating the structure-function of the primate CL (28). We found that PRLR mRNA was significantly highest in the mid through late stages, and protein levels peaked at the mid-late to late stages, supporting the possibility of a role for prolactin in regulating primate luteal function. Many other genes in the hormone and growth factor signaling group represent pathways that have received little or no attention in luteal physiology, but that may have physiological significance due to their differential expression. Such examples of differentially expressed genes identified in the present microarray study include the following: EDNRB, angiotensin II receptor (AGTR1), natriuretic peptide receptor C (NPR3), retinoic acid receptor responder-3 (RARRES3), purinergic receptor P2Y-5 (P2RY5), and serotonin receptor 2B (HTR2B), as well as several orphan G protein-coupled receptors (e.g. GPR177, GPR113, GPR116).

The factors controlling functional regression in the primate CL are not known (2). There appears to be a decrease in responsiveness of the primate CL to LH as it ages (29, 30, 31). Our data indicate that this is not due to a decrease in expression of LHCGR because its mRNA levels were highest at the midlate and late stages. Therefore, other factors may cause the loss of CL responsiveness to LH in the late luteal phase and subsequent functional regression such as an uncoupling of LHCGR to adenylate cyclase signaling (32).

It seems likely that a decrease in expression of steroidogenic genes would be associated with functional regression of the CL. Although there were significant decreases in mRNA levels for SCARB1 and HSD3B2 in late CL compared with earlier stages, it was somewhat surprising that more steroidogenic-related genes were not differentially expressed. Levels of mRNA for low-density lipoprotein receptor (LDLR), steroidogenic acute regulatory protein (STAR), and CYP11A1 were significantly lower in very-late CL compared with all other stages. Because these CL were not collected until after progesterone secretion had already ceased, factors other than a decrease in expression of steroidogenic-related genes may cause functional regression of the primate CL.

Another factor that may contribute to luteolysis in the primate is estradiol. It has long been hypothesized that estradiol produced by the CL may serve as a self-destruct signal (28). CYP19A1 mRNA and protein levels were significantly up-regulated from the early to mid stage and remained high through the late stage. This indicates an increased capacity of the CL to produce estradiol once it develops, and estradiol may be important for regulating luteal function or other biological processes. The possibility of estradiol as a local luteolytic factor has been supported by the discovery of estrogen receptor-β in primate luteal tissue with peak mRNA and protein expression occurring during the mid-late luteal phase (7), which coincides with peak mRNA and protein expression of CYP19A1 found in the current study. Alternatively, estrogen may play a luteotropic role because it has been reported in mice that prostaglandin F2{alpha} inhibits CYP19A1 mRNA and protein expression before the onset of luteolysis at the end of pregnancy, which may play an important role in the luteolytic process (33, 34). Additional studies to define the role of estrogen in the primate CL are needed.

Dynamic regulation of genes encoding proteins involved in luteotropic and luteolytic prostaglandin synthesis, action, and signaling during the life span of the primate CL was also noted in the microarray database. Specifically, microarray and Q-PCR results are consistent with a potential requirement for prostaglandin E2 synthesis and signaling during luteinization and maintenance of the CL, which is then followed by a switch to an environment that favors prostaglandin F2{alpha} signaling as the CL nears the time of luteolysis. Further studies are needed to clarify the roles of prostaglandin E2 and prostaglandin F2{alpha} as luteotropic and luteolytic prostaglandins, respectively, in the primate CL.

The present study is the first to provide a systematic analysis of gene expression for the entire rhesus macaque genome in the CL during the luteal phase of the natural menstrual cycle. The mRNA levels of several genes were quantified with a secondary method to validate the accuracy of the resultant microarray database. Our Q-PCR results had a greater than 94% agreement with microarray in terms of expression patterns (17 of 18 genes analyzed). In a few cases, we further determined that changes detected in mRNA translate into corresponding changes in protein levels. Therefore, this database will facilitate the identification of many novel or previously unappreciated pathways that regulate the structure and function of the primate CL.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
REFERENCES
 
Animal Care and Hormone Assays
The general care and housing of rhesus monkeys (Macaca mulatta) at the Oregon National Primate Research Center (ONPRC) has been described previously (35). Blood samples were collected daily by saphenous venipuncture starting 6 d after onset of menses until the day of lutectomy. Serum was separated and samples were assayed daily for estradiol and progesterone concentrations by a specific electrochemiluminescent assay using an IMMULITE 2000 (Seimens Medical Solutions, Malvern, PA) by the Endocrine Services Laboratory at the ONPRC (36). After the midcycle estradiol surge, the first day of low serum estradiol (< 100 pg/ml) corresponds with the day after the LH surge (LH surge = d 0) (37). The protocol for tissue collection and animal treatment was approved by the ONPRC Animal Care and Use Committee and met the standards in the NIH Guidelines for the Care and Use of Laboratory Animals.

CL Collection during the Natural Menstrual Cycle
Monkeys were anesthetized and CL collected during aseptic midline laparotomy (2). The CL were collected (n = 4 CL per group) between d 3–5 (early stage, developing CL), d 7–8 (mid stage, fully functional CL), d 10–12 (mid-late stage, functional CL on the verge of regression), d 14–16 (late stage, functionally regressing CL), or d 18–19 (very-late stage, menses) after the LH surge as previously described and characterized (1, 2, 3, 4, 5, 6). CL were sectioned, flash frozen in liquid nitrogen, and stored at –80 C for subsequent isolation of RNA and protein.

RNA Extraction and Gene Microarray
Total RNA was extracted using Trizol reagent (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions and further purified using RNeasy spin columns (QIAGEN, Chatsworth, CA). Final RNA concentrations and purity were determined by spectrophotometry. The integrity of RNA samples was determined using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA), and all samples were deemed of high quality. Microarray assays were performed in the Affymetrix Microarray Core of the Oregon Health & Science University (OHSU) Gene Microarray Shared Resource. Individual RNA samples were used to produce labeled cRNA that was hybridized to GeneChip Rhesus Macaque Genome Arrays (Affymetrix, Inc.). GeneChip operating system version 1.4 software (Affymetrix) was used to process images and generate probe level measurements (.cel files). See supplemental data published on The Endocrine Society’s Journals Online web site at http://mend.endojournals.org for all information necessary for Minimum Information About Microarray Experiments (MIAME)-compliance.

Microarray Data Analysis
The processed image files were normalized across arrays using the robust multichip average algorithm (38) and log transformed (base 2), thus allowing direct comparison of probe set values between all samples used in the experiment. After normalization, GeneSifter (VizX Labs, Seattle, WA) microarray expression analysis software was used to identify differentially expressed transcripts. With the early CL serving as the baseline reference, transcripts that exhibited a greater than 2-fold change (ANOVA, P < 0.05; Benjamini and Hochberg correction for false discovery rate) were considered differentially expressed. See supplemental data for filtered lists of differentially expressed transcripts.

The GeneSifter microarray analysis software used to identify differentially expressed genes only determines whether there is a statistically significant change relative to the baseline, but does not specify which groups are different from each other. Therefore, to facilitate in-depth comparison of the microarray results, the processed image files were again normalized with the robust multichip average algorithm and log transformed (base 2) using the Affymetrix Expression Console version 1.0. The corresponding microarray expression data were analyzed by one-way ANOVA and pairwise differences determined with the Student-Newman-Keuls (SNK) test (P < 0.05).

Differentially expressed genes were grouped based on objective and subjective analysis of the gene lists. The list of differentially expressed genes was divided into two clusters: 1) up-regulated transcripts, and 2) down-regulated transcripts (relative to early CL baseline). Ontology reports (molecular function) were generated from the two clusters and assigned a z-score (12) by GeneSifter, with a score greater than 2.0 and a minimum of five genes being considered significant (12).

Taqman Q-PCR
The RNA samples used for microarray analysis were used to synthesize cDNA as described previously (39). Sequences corresponding to the various probe sets on the microarray chip (available at http://www.affymetrix.com/analysis/index.affx) were used to BLAST the rhesus macaque genome sequence to identify the corresponding full-length cDNA. The complete cDNA sequence was subsequently used to design the Q-PCR primers and Taqman MGB probes using PrimerExpress software (Applied Biosystems, Foster City, CA). Primers and probes were purchased from Invitrogen and Applied Biosystems, respectively. The Q-PCR reaction was performed as described previously with minor modifications (39). See supplemental data for detailed description of the reverse transcription and Q-PCR procedures, plus sequences of all primers and probes used.

Relative concentrations of the target genes were normalized to 18S rRNA levels, and the ratios were log transformed before statistical analysis. Data were analyzed using one-way ANOVA followed by pairwise comparisons with the SNK test, and differences were considered statistically significant at P < 0.05.

Western Blot Analysis
Frozen CL were homogenized in 600 µl of homogenization buffer [50 mM Tris-HCl, 150 mM NaCl, 10% glycerin, 1% Triton X-100, and Halt protease inhibitor cocktail (Pierce Chemical Co., Rockford, IL)], and centrifuged at 10,000 x g for 10 min at 4 C. Protein concentrations were determined by Lowry protein assay (Bio-Rad Laboratories, Inc., Hercules, CA) following the manufacturer’s recommendations. Antibodies against PRLR (R&D Systems, Minneapolis, MN; MAB1167), HSD3B2 (Santa Cruz Biotechnology, Inc., Santa Cruz, CA; sc-30821), and CYP19A1 (Novus Biologicals, Littleton, CO; NB100–1596) were purchased commercially.

For PRLR and CYP19A1, 40 µg of protein was resolved on 10% Tris-HCl or 4–15% Tris-HCl gradient Criterion precast gels (Bio-Rad), respectively. For HSD3B2, 25 µg of protein was resolved on 10% Tris-HCl gels. Proteins were transferred to nitrocellulose membranes at 20 V overnight at 4 C. Membranes were blocked for 1 h at room temperature in Tris-buffered saline (TBS) containing 0.25% Tween 20 (TTBS) and 5% nonfat dry milk. Antibodies (2 µg/ml PRLR, 0.8 µg/ml CYP19A1, 0.3 µg/ml HSD3B2) were added in TTBS with 0.5–1.0% nonfat dry milk, and incubated overnight at 4 C. If available, immunizing peptide was used to preabsorb the primary antibody by mixing a 4:1 peptide-antibody ratio and incubating at room temperature in TTBS for 2 h before membrane exposure. After incubation with primary antibody, membranes were washed with four changes of TTBS for 5 min each. Membranes were then incubated with an appropriate secondary antibody (Santa Cruz Biotechnology, 1:4000 dilution) for 1 h at room temperature. Membranes were washed as before, developed with enhanced chemiluminescent substrate (Santa Cruz Biotechnology), and exposed to x-ray film. After detection of target protein, membranes were stripped in 20 ml of Restore Western blot stripping buffer (Pierce) for 1 h with alternating incubations at room temperature and 65 C, followed by washing. Membranes were reprobed for β-tubulin (Santa Cruz, sc-9104) to serve as a loading control.

Films were scanned and densitometry analysis performed using Quantity One version 4.3.1 software (Bio-Rad). The background-adjusted volume of each band was normalized to β-tubulin for each sample. Data were log transformed if necessary. The ratio of target protein/β-tubulin for each stage was analyzed by one-way ANOVA followed by pairwise comparison using the SNK test with differences considered significant at P < 0.05.


   ACKNOWLEDGMENTS
 
We thank the following core facilities at the ONPRC: endocrine services (Dr. Richard Yeoman), molecular biology (Dr. Yibing Jia), Affymetrix microarray core (Dr. Chris Harrington), and the Division of Animal Resources.


   FOOTNOTES
 
This research was supported by the following grants from National Institutes of Health: R01 HD20869 (to R.L.S.), U54 HD18185 (to R.L.S.), U54 HD55744 (to R.L.S. and J.D.H.), R01 HD42000 (to J.D.H.), RR00163 (to R.L.S. and J.D.H.), and a T32 training grant HD007133 (to R.L.B.).

Disclosure Summary: The authors have nothing to disclose.

First Published Online February 7, 2008

Abbreviations: CL, Corpus luteum; CYP, cytochrome P450; HSD, hydroxysteroid dehydrogenase; PRLR, prolactin receptor; Q-PCR, quantitative real-time PCR; SNK, Student-Newman-Keuls; TBS, Tris-buffered saline; TTBS, TBS containing 0.25% Tween 20.

Received for publication October 22, 2007. Accepted for publication January 28, 2008.


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 MATERIALS AND METHODS
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